using PyPlot
using NtToolBox
# using Autoreload
# arequire("NtToolBox")

n0 = 512
f = rescale(load_image("NtToolBox/src/data/lena.png", n0));

figure(figsize = (5,5))
imageplot(f, "Image f");

figure(figsize = (5,5))
imageplot(f[Int(n0/2 - 32) : Int(n0/2 + 32), Int(n0/2 - 32) : Int(n0/2 + 32)], "Zoom")

figure(figsize = (8,8))
imageplot(-f, "-f", [1, 2, 1])
imageplot(f[end:-1:1, :], "Flipped", [1, 2, 2])

k = 9; #size of the kernel
h = ones(k, k)
h = h/sum(h); #normalize

fh = conv2(Array{Float64, 2}(f), h);

figure(figsize = (5,5))
imageplot(fh, "Blurred image")

F = plan_fft(f)
F = (F*f)/n0;

println(@sprintf("Energy of Image:   %f", norm(f)))
println(@sprintf("Energy of Fourier:   %f", norm(F)))

L = fftshift(log(abs(F) + 1e-1));

figure(figsize = (5,5))
imageplot(L, "Log(Fourier transform)")

M = Base.div(n0^2, 64);

include("NtSolutions/introduction_4_fourier_wavelets/exo1.jl")

## Insert your code here.

figure(figsize = (7, 6))

subplot(2, 1, 1)
plot(f[: , Base.div(n0, 2)])
xlim(0, n0)
title("f")

subplot(2, 1, 2)
plot(fM[: , Base.div(n0, 2)])
xlim(0, n0)
title("f_M")

show()

T = .2;

F = plan_fft(f)
F = (F*f)/n0;

FT = F .* (abs(F) .> T);

L = fftshift(log(abs(FT) + 1e-1))
figure(figsize = (5,5))
imageplot(L, "thresholded Log(Fourier transform)")

fM = plan_ifft(FT)
fM = real(n0*(fM*FT));

figure(figsize = (5,5))
imageplot(clamP(fM), @sprintf("Linear, Fourier, SNR = %.1f dB", snr(f, fM)))

m = sum(FT .!= 0)
print(@sprintf("M/N = 1/%d" ,(n0^2)/m))

include("NtSolutions/introduction_4_fourier_wavelets/exo2.jl")

## Insert your code here.

Jmin = 0;

fw = NtToolBox.perform_wavelet_transf(f, Jmin, + 1);

# using NPZ
# test = npzread("pgsh.npy")

figure(figsize = (10, 10))

NtToolBox.plot_wavelet(fw)
title("Wavelet coefficients")

include("NtSolutions/introduction_4_fourier_wavelets/exo3.jl")

## Insert your code here.

T = .15

fwT = fw.*(abs(fw) .> T);

figure(figsize = (15,15))

subplot(1, 2, 1)
plot_wavelet(fw)
title("Original coefficients")

subplot(1, 2, 2)
plot_wavelet(fwT)
title("Thresholded coefficients")

show()

fM = NtToolBox.perform_wavelet_transf(fwT, Jmin, -1)

figure(figsize = (5, 5))
imageplot(clamP(fM), @sprintf("Approximation, SNR,  = %.1f dB", snr(f, fM)))

include("NtSolutions/introduction_4_fourier_wavelets/exo4.jl")

## Insert your code here.

figure(figsize = (7, 6))

subplot(2, 1, 1)
plot(f[:, Base.div(n0, 2)])
title("f")

subplot(2, 1, 2)
plot(fM[:, Base.div(n0, 2)])
title("f_M")

show()
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